Stat 305: Linear Models (and more)

Overview

This course is about regression methods. In regression we're working primarily with real valued responses. The main tool for regression is the linear model, in all it's glory ranging from the humble one sample t test to more elaborate methods like splines and wavelets. We also look at competing methods that are sometimes better than linear regression, because the focus is on the problems not the tools.

There will be about 5 problem sets and a final exam. Students are expected to use R to do the problem sets.

The final exam is a take home due on Monday December 12 at 11:30.

Here is the syllabus

Classes

MWF 1:15-2:05 Room 300-300

Instructor

Art Owen
Sequoia Hall 130
My userid is owenbuzzard on stanfordbuzzard.edu (remember to remove the carrion eaters)
Office: Tue, 2:15-3:05 (except Sept 27!)

TAs


Texts

The main text is "Applied Linear Regression" by Weisberg
May be available online to Stanford users here or here .

The supplementary text is ``Introductory Statistics with R'' by Peter Dalgaard.
Available online from Stanford accounts here.

That book explains how to use R. If you already know how to use R you don't need to buy it. There are R tutorials below as well.


Problems

Problems (closed for the season)
...also the existence of a new problem set will be announced in class

Be sure to give Axess a working email address:
I expect to send a small number of important emails about problem sets and the homework there. Most other announcements will be made in class. If you email me about the class, be sure to have stat 305 in your subject line. Otherwise, your email won't show when I search for course related emails.
Late penalties apply:
We will count days late on each problem set. Each day late is penalized by 10% of the homework value. Homework more than 3 days late will ordinarily get 0. If you're travelling, you can email a pdf file. For sickness, interviews and other events, up to 3 late days total are forgiven at the end of the quarter. (Work late enough to get zero does not get redeemed though.)

Supplementary materials

Big picture

Statistical material

Subject matter material